Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{196204,
        author = {M Hemanth and S Bharath kumar and N Prakash and K Srikanth Reddy and D Mamatha},
        title = {CREDIT CARD FRAUD DETECTION},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {1736-1740},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=196204},
        abstract = {This study presents a machine-learning-based system designed for the real-time identification of credit card fraud in digital transactions. As online payments continue to grow rapidly, the demand for fast and reliable fraud detection has become critical. A primary challenge in this domain is the severe class imbalance present in transaction data, where legitimate records vastly outnumber fraudulent ones.
The framework incorporates comprehensive data preprocessing, targeted feature engineering, and class balancing through the Synthetic Minority Oversampling Technique (SMOTE). Two supervised learning algorithms—Decision Tree and Random Forest—were implemented and thoroughly assessed using standard evaluation metrics. Results show that the Random Forest classifier achieves superior performance across precision, recall, F1-score, and overall accuracy, while also minimizing false positives. The proposed approach is efficient, scalable, and ready for integration into banking and financial platforms to enhance security and reduce losses.},
        keywords = {},
        month = {April},
        }